Tag: critical thinking skills

The AI Teammate: Three Roles to Build Student AI Fluency – Faculty Focus
AI is now embedded in teaching and learning. As educators, how do we help students benefit from AI without slipping into dependency, surface-level work, or ethical misconduct? I’ve found a helpful way to clarify conversations with students (and with myself) by thinking of AI as a teammate (or teammates) with clearly defined roles: the Tasker, the Draftsmith, and the Facilitator.

Imagine a meeting of a team of people: someone handles logistics, someone sketches ideas, someone pushes discussion. Framing AI this way has made it easier to talk about students remaining responsible for how and to what purposes they use AI. My approach draws on recent scholarship about AI reshaping how teams work (Dell’Acqua et al., 2025).

The AI Teammates

The Tasker

The Tasker deals with repetitive or procedural work such as managing our calendars, formatting citations, or cleaning datasets. These tasks often are tedious but essential and using AI frees time that can be focused on deeper thinking. To use a Tasker, we can set up a Robotic Process Automation (RPA) with AI, use embedded AI tools such as Copilot in MS:Word or Claude in MS:Excel or Google sheets, or create what is currently referred to as an LLM “Agent.” As Farri & Rosani (2025) describe in their HBR guide to generative AI for managers, we can use AI to lighten the load on procedural chores, affording us the time and energy to engage more with deeper thinking and learning.

Because AI output may contain mistakes, students must learn to identify the levels of risks they believe are acceptable for the given task before turning to AI and must validate its output. The level of validation required may depend on the risk level, needed accuracy and quality, and current knowledge of the tool’s metrics.

We approach the Tasker differently than we do the Draftsmith and Facilitator: we can set our RPA or Agent up to run and only involve ourselves in its applications until we’re sure it can work independently and accurately, or we can trigger the Tasker each time we need it. Students must learn to set aside sufficient validation time before designing a Tasker.

The Draftsmith

The Draftsmith can spot passive voice in students’ writing and explain why it’s problematic, convert an outline into a draft of a PowerPoint, and produce study tools such as multiple choice or essay questions, podcasts, and videos from their course notes. My engineering students have prompted their Draftsmiths using a prompt library I created specifically for them to help them convert their design clients’ stated needs into project requirements. Bussgang (2025) has pointed out how small steps like these can dramatically boost productivity without sacrificing learning.

While AI may generate text or ideas, the final product must be the student’s own (Duffy, 2025). I teach my students that, much to their dismay, they must develop their own writers’ voice before using AI and that this process can take years and many writing efforts! Our writer’s voice presents us to our bosses, colleagues, and clients given that much of our communication at work is through emails. College is the best time to invest in this effort because it’s rare to get opportunities later to do so.

From students’ comments, I believe they use AI in the Draftsmith role more frequently than the other two roles. Few realize the Draftsmith increases cognitive load when they use it correctly and decreases their learning when they use it incorrectly!

It increases the load because they must provide the AI with significant context for it to provide anything other than banal, common prose that flattens their own voice (Purohit, 2025) or “creative” prose that is meaningless. AI use decreases their learning when they use it to write first and second drafts because writing sharpens our thinking and embeds learned concepts into our long-term memory.

The Facilitator

Instead of using AI to generate content (the Draftsmith), the Facilitator is a use of AI as a thinking partner (Solis, 2024 and HBSP’s ManageMentor course 2025). A student might ask AI to play devil’s advocate, suggest counterarguments, or pose probing questions when their paper’s argument needs strengthening. AI might help simulate a stakeholder’s perspective with whom the student role plays to prepare for a meeting or consider how their ideas might land with their student group peers. My students have used AI in the Facilitator role to pressure test their design ideas, review their research plans, and prepare to give presentations. When students use the Facilitator, they’re reflecting (Harbridge, 2025) and thereby deepening their learning.

To get the best value from AI as a Facilitator, the student must approach it with the expectation that it will take considerable time. The Facilitator provides meaningful interaction only when the student gives it their full attention, asks probing questions, provides significant context, and is patient. They may need to iterate, restating their prompts or providing more information, and to ask the LLM to be candid and critical. They may need to step away from the LLM, perhaps try a different one or come back on another day, when it goes off in the wrong direction.

We have become too used to scanning an LLM’s output. Just as I would not expect a student who came to me for career advice to start scrolling on their phone while I’m talking, I must teach them to give their full attention to the Facilitator.

What I’m suggesting here is not a list of use cases. Instead, I’m providing students with an approach to using AI where they plan before typing into the chat box, see its best use as a Facilitator that partners with them in learning and development, and recognize that validation and reading output will be time and cognitively intensive.

What This Means for Teaching

I’ve found that when I teach these roles to students before assigning homework, they make better choices regarding when and how to use AI. Here are some tactics I’ve tried that have netted positive results:
- Design assignments that invite different roles. For example, ask students to begin with AI as a Tasker (perhaps organizing sources or cleaning a dataset), then shift to using the Draftsmith to help them find passive voice or other syntax issues in a paper, and finally draw on the Facilitator to find the holes in their arguments before submitting a final paper. Intentionally stating the role each assignment calls for and requiring them to state when they’re using the Tasker, Draftsmith, or Facilitator solidifies their understanding that their approach to AI differs by role.
- Be clear about where AI helps and where it doesn’t. AI can help with structure, suggestions, and ideation, but it cannot replace revision, critical thinking, or the development of one’s own voice. This must be intentionally taught by including questions requiring students to reflect on what worked and where the AI led them astray in any assignments allowing AI use.
- Encourage disclosure. I talk extensively about “Total AI Transparency”, ensuring students that I will note any use of AI in my communications with them and I expect the same from them. Trust has broken down as both students and instructors believe the other is using AI when they’re not and failing to recognize when they are. Owning up to my own use and being clear on why I used AI for that task encourages students to do the same.
Closing Thoughts

We need to steer students away from extremes of either refusing to use AI at all or letting it rob them of learning. By distinguishing the roles of Tasker, Draftsmith, and Facilitator, we clarify our expectations of students. More importantly, we help them become more deliberate in their approach, preparing them to be AI-fluent.

Illysa Izenberg is an Associate Teaching Professor for the Center for Leadership Education in the Whiting School of Engineering at The Johns Hopkins University. She has been teaching management and business ethics to graduate and undergraduate students via Face-to-Face, online, and blended courses since 2006. Izenberg earned her MBA from the Harvard Graduate School of Business and is the winner of both the Alumni and Pond Excellence in Teaching Awards (2016 and 2020 respectively).

References

Bussgang, J. (2025, May 20). 3 small steps to 10x your productivity with AI this week. The Experimentation Machine. https://experimentationmachine.com/p/10x-this-week

Duffy, A. (2025, April 11). Your CEO just said ‘Use AI or else.’ Here’s what to do next. Every. https://every.to/p/your-ceo-just-said-use-ai-or-else-here-s-what-to-do-next

Farri, E., & Rosani, G. (2025). HBR guide to generative AI for managers. Harvard Business Review Press.

Gross, A. (2025). AI Crash Course [Online course]. SectionAI. https://www.sectionschool.com/courses/ai-crash-course-workshop

Harbridge, R. (2025). AI for Team Leaders [Online course]. SectionAI.

Harvard Business Publishing. (2025). Help your team harness generative AI (Lesson 1). In Leading with generative AI [Online course]. Harvard ManageMentor.

Purohit, R. (Host). (2025, June 18). The man inside the minds of the people building AGI [Audio podcast episode]. In AI & I. Every.

Solis, B. (2024, November). Train your brain to work creatively with Gen AI. Harvard Business School Publishing.
Source link
November 17, 2025
The AI Teammate: Three Roles to Build Student AI Fluency – Faculty Focus
AI is now embedded in teaching and learning. As educators, how do we help students benefit from AI without slipping into dependency, surface-level work, or ethical misconduct? I’ve found a helpful way to clarify conversations with students (and with myself) by thinking of AI as a teammate (or teammates) with clearly defined roles: the Tasker, the Draftsmith, and the Facilitator.

Imagine a meeting of a team of people: someone handles logistics, someone sketches ideas, someone pushes discussion. Framing AI this way has made it easier to talk about students remaining responsible for how and to what purposes they use AI. My approach draws on recent scholarship about AI reshaping how teams work (Dell’Acqua et al., 2025).

The AI Teammates

The Tasker

The Tasker deals with repetitive or procedural work such as managing our calendars, formatting citations, or cleaning datasets. These tasks often are tedious but essential and using AI frees time that can be focused on deeper thinking. To use a Tasker, we can set up a Robotic Process Automation (RPA) with AI, use embedded AI tools such as Copilot in MS:Word or Claude in MS:Excel or Google sheets, or create what is currently referred to as an LLM “Agent.” As Farri & Rosani (2025) describe in their HBR guide to generative AI for managers, we can use AI to lighten the load on procedural chores, affording us the time and energy to engage more with deeper thinking and learning.

Because AI output may contain mistakes, students must learn to identify the levels of risks they believe are acceptable for the given task before turning to AI and must validate its output. The level of validation required may depend on the risk level, needed accuracy and quality, and current knowledge of the tool’s metrics.

We approach the Tasker differently than we do the Draftsmith and Facilitator: we can set our RPA or Agent up to run and only involve ourselves in its applications until we’re sure it can work independently and accurately, or we can trigger the Tasker each time we need it. Students must learn to set aside sufficient validation time before designing a Tasker.

The Draftsmith

The Draftsmith can spot passive voice in students’ writing and explain why it’s problematic, convert an outline into a draft of a PowerPoint, and produce study tools such as multiple choice or essay questions, podcasts, and videos from their course notes. My engineering students have prompted their Draftsmiths using a prompt library I created specifically for them to help them convert their design clients’ stated needs into project requirements. Bussgang (2025) has pointed out how small steps like these can dramatically boost productivity without sacrificing learning.

While AI may generate text or ideas, the final product must be the student’s own (Duffy, 2025). I teach my students that, much to their dismay, they must develop their own writers’ voice before using AI and that this process can take years and many writing efforts! Our writer’s voice presents us to our bosses, colleagues, and clients given that much of our communication at work is through emails. College is the best time to invest in this effort because it’s rare to get opportunities later to do so.

From students’ comments, I believe they use AI in the Draftsmith role more frequently than the other two roles. Few realize the Draftsmith increases cognitive load when they use it correctly and decreases their learning when they use it incorrectly!

It increases the load because they must provide the AI with significant context for it to provide anything other than banal, common prose that flattens their own voice (Purohit, 2025) or “creative” prose that is meaningless. AI use decreases their learning when they use it to write first and second drafts because writing sharpens our thinking and embeds learned concepts into our long-term memory.

The Facilitator

Instead of using AI to generate content (the Draftsmith), the Facilitator is a use of AI as a thinking partner (Solis, 2024 and HBSP’s ManageMentor course 2025). A student might ask AI to play devil’s advocate, suggest counterarguments, or pose probing questions when their paper’s argument needs strengthening. AI might help simulate a stakeholder’s perspective with whom the student role plays to prepare for a meeting or consider how their ideas might land with their student group peers. My students have used AI in the Facilitator role to pressure test their design ideas, review their research plans, and prepare to give presentations. When students use the Facilitator, they’re reflecting (Harbridge, 2025) and thereby deepening their learning.

To get the best value from AI as a Facilitator, the student must approach it with the expectation that it will take considerable time. The Facilitator provides meaningful interaction only when the student gives it their full attention, asks probing questions, provides significant context, and is patient. They may need to iterate, restating their prompts or providing more information, and to ask the LLM to be candid and critical. They may need to step away from the LLM, perhaps try a different one or come back on another day, when it goes off in the wrong direction.

We have become too used to scanning an LLM’s output. Just as I would not expect a student who came to me for career advice to start scrolling on their phone while I’m talking, I must teach them to give their full attention to the Facilitator.

What I’m suggesting here is not a list of use cases. Instead, I’m providing students with an approach to using AI where they plan before typing into the chat box, see its best use as a Facilitator that partners with them in learning and development, and recognize that validation and reading output will be time and cognitively intensive.

What This Means for Teaching

I’ve found that when I teach these roles to students before assigning homework, they make better choices regarding when and how to use AI. Here are some tactics I’ve tried that have netted positive results:
- Design assignments that invite different roles. For example, ask students to begin with AI as a Tasker (perhaps organizing sources or cleaning a dataset), then shift to using the Draftsmith to help them find passive voice or other syntax issues in a paper, and finally draw on the Facilitator to find the holes in their arguments before submitting a final paper. Intentionally stating the role each assignment calls for and requiring them to state when they’re using the Tasker, Draftsmith, or Facilitator solidifies their understanding that their approach to AI differs by role.
- Be clear about where AI helps and where it doesn’t. AI can help with structure, suggestions, and ideation, but it cannot replace revision, critical thinking, or the development of one’s own voice. This must be intentionally taught by including questions requiring students to reflect on what worked and where the AI led them astray in any assignments allowing AI use.
- Encourage disclosure. I talk extensively about “Total AI Transparency”, ensuring students that I will note any use of AI in my communications with them and I expect the same from them. Trust has broken down as both students and instructors believe the other is using AI when they’re not and failing to recognize when they are. Owning up to my own use and being clear on why I used AI for that task encourages students to do the same.
Closing Thoughts

We need to steer students away from extremes of either refusing to use AI at all or letting it rob them of learning. By distinguishing the roles of Tasker, Draftsmith, and Facilitator, we clarify our expectations of students. More importantly, we help them become more deliberate in their approach, preparing them to be AI-fluent.

Illysa Izenberg is an Associate Teaching Professor for the Center for Leadership Education in the Whiting School of Engineering at The Johns Hopkins University. She has been teaching management and business ethics to graduate and undergraduate students via Face-to-Face, online, and blended courses since 2006. Izenberg earned her MBA from the Harvard Graduate School of Business and is the winner of both the Alumni and Pond Excellence in Teaching Awards (2016 and 2020 respectively).

References

Bussgang, J. (2025, May 20). 3 small steps to 10x your productivity with AI this week. The Experimentation Machine. https://experimentationmachine.com/p/10x-this-week

Duffy, A. (2025, April 11). Your CEO just said ‘Use AI or else.’ Here’s what to do next. Every. https://every.to/p/your-ceo-just-said-use-ai-or-else-here-s-what-to-do-next

Farri, E., & Rosani, G. (2025). HBR guide to generative AI for managers. Harvard Business Review Press.

Gross, A. (2025). AI Crash Course [Online course]. SectionAI. https://www.sectionschool.com/courses/ai-crash-course-workshop

Harbridge, R. (2025). AI for Team Leaders [Online course]. SectionAI.

Harvard Business Publishing. (2025). Help your team harness generative AI (Lesson 1). In Leading with generative AI [Online course]. Harvard ManageMentor.

Purohit, R. (Host). (2025, June 18). The man inside the minds of the people building AGI [Audio podcast episode]. In AI & I. Every.

Solis, B. (2024, November). Train your brain to work creatively with Gen AI. Harvard Business School Publishing.
Source link
November 17, 2025
Teaching with AI: From Prohibition to Partnership for Critical Thinking – Faculty Focus
What if the AI tools we are trying to limit and caution against were actually essential (or beneficial) to enhancing the critical thinking skills we are afraid of losing? Since the 2023 proliferation of generative AI, faculty have been inundated with warnings that human intelligence is being eroded by AI. As a result, some faculty have adopted policies banning or severely limiting AI use driven by concerns of academic integrity and the fear of students bypassing essential learning.

However, banning AI will not prevent students from using it, whether for nefarious or appropriate purposes. Instead, it may deny students a chance to practice and engage with AI in an educational setting where they and faculty can explore its full potential collaboratively. This kind of restrictive thinking is based on two flawed assumptions: that AI cannot support student thinking and that students will only use AI to cheat. The challenge is not to police every use, but to reframe our approach from one of prohibition to one of collaborative partnership.

This shift in perspective allows faculty to systematically integrate AI into courses in a developmentally appropriate manner. By centering policies on learning, we can encourage students to take an active, self-regulated approach to their education. This reframes the focus from dishonesty to autonomous learning, emphasizing academic values while scaffolding meaningful assignments that challenge student thinking.

Integrating AI into the curriculum requires a developmental approach, much like teaching toddlers. Expecting a first-year student to rely on AI for essential skills development is like asking a toddler to color within the lines—it’s developmentally inappropriate. Instead, our policies should align with a student’s progression. In lower-level courses, the focus must be on foundational skill-building including learning how to use AI. For upper-level and graduate students, we can empower them to autonomously evaluate AI’s role in their learning and whether or not AI is developing or replacing learning. Meanwhile, mid-level courses can provide a scaffolded transition, with specific instructions on how and when to use AI.

It is important to consider students’ prior knowledge of AI as well. While many are comfortable using technology and AI, they may lack metacognitive awareness of how their use affects learning. Understanding students’ technology usage is crucial when designing courses. Ultimately, use your best judgment—some graduate courses may require a cautious approach, while entry-level courses might benefit from a more permissive policy, especially since students within the same course can have a wide range of AI abilities.

How to Integrate AI Developmentally into Your Courses
- Lower-Level Courses: Focus on building foundational skills, which includes guided instruction on how to use AI responsibly. This moves the strategy beyond mere prohibition.
- Mid-Level Courses: Use AI as a scaffold where faculty provide specific guidelines on when and how to use the tool, preparing students for greater independence.
- Upper-Level/Graduate Courses: Empower students to evaluate AI’s role in their learning. This enables them to become self-regulated learners who make informed decisions about their tools.
- Balanced Approach: Make decisions about AI use based on the content being learned and students’ developmental needs.
Now that you have a framework for how to conceptualize including AI into your courses here are a few ideas on scaffolding AI to allow students to practice using technology and develop cognitive skills.

To introduce AI into your course, create a prompt that asks students to have a conversation with an AI about a concept you will be discussing. This anticipatory set can prime student thinking and encourage them to use AI in a conversational manner, moving beyond simply asking for answers. You can then discuss the AI’s responses—exploring bias, hallucinations, and the depth of its answers—which naturally leads to a conversation about crafting better prompts. This simple, ungraded exercise allows all students to participate, provides valuable practice, and serves a clear learning purpose.

Another example of a nongraded, purposeful use of AI is for providing feedback on learning. When students are writing papers, you can create custom AI agents to provide feedback on different parts of the writing process, from idea development to final submission. These agents can be designed to follow assignment criteria without writing any portion of the work. If students choose to use the agent, you can ask them to share their feedback conversations to assess the quality of the feedback and to write about how they incorporated it to develop their ideas—adding a crucial element of metacognition.

This method is also ideal for reinforcing and mastering skill development. For example, in a counseling course, students can practice articulating confidentiality and its limits to a fictional client created by AI. Once this foundational skill is mastered, the agent can be instructed to demonstrate signs of self-harm, allowing the student to practice assessing client safety and deciding whether to break confidentiality. As with the other examples, you can ask students to share their conversations for your feedback, have them critique their own performance, and provide a rationale for their approach. The agent itself can even provide feedback at the end of the session.

Finally, a higher level of AI integration is to have students create their own custom learning AI agent. By this point, students will have had multiple chances to improve their prompt writing, practice using AI for learning (not bypassing skills), and evaluate how AI supports their development. Creating a personalized study agent would be an ideal way for students to be active in their learning and assess the areas they need to develop. Faculty could provide guidelines and ask students to share how they are using AI positively.

How to Scaffold AI into Your Courses
- Start with low-stakes, ungraded activities.
- Then use AI to provide meaningful, real-time feedback.
- Create opportunities for skill reinforcement and mastery.
- Finally, empower students to become creators, not just users.
To truly prepare students for life after graduation, institutions and faculty must be willing to provide training on new technologies with a lens of learning. Faculty must move beyond fear and prohibition and engage directly with these tools. By exploring AI’s potential faculty can transform their teaching from a place of restriction to one of collaborative partnership.

By taking a development and scaffolded approach to AI implementation students can benefit from its potential. If this approach is embraced, the question is no longer “Should we allow AI in the classroom?” The more productive question becomes “How can we teach our students to become discerning and effective creators of a world shaped by AI?”

Michael Kiener, PhD, CRC, is a professor at Maryville University of St. Louis in their Clinical Mental Health Counseling program. For the past 10 years he has coordinated their Scholarship of Teaching and Learning Program, where faculty participate in a yearlong program with a goal of improved student learning. In 2012 and 2024 he received the Outstanding Faculty Award for faculty who best demonstrate excellence in the integration of teaching, scholarship and/or service. He has over thirty publications including a co-authored book on strength-based counseling and journal articles on career decision making, action research, counseling pedagogy, and active and dynamic learning strategies.
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November 5, 2025
Regurgitative AI: Why ChatGPT Won’t Kill Original Thought – Faculty Focus

Parents who grew up in the ’80s and ’90s know the feeling: you’re listening to your kid’s playlist, and suddenly a song hits you with a wave of uncanny familiarity. Despite the claims by your teen that it is the latest and greatest, you know that it is just a repackaging of one of your favorite tunes from the past. I am noticing a similar trend with generative AI. It is inherently regurgitative: reshaping and repackaging ideas and thoughts that are already out there.

Fears abound as to the future of higher education due to the rise of generative AI. Articles from professors in many different fields predict that AI is going to destroy the college essay or even eliminate the need for professors altogether. Their fears are well founded. Seeing the advances that generative AI has made in just the past few months, I am constantly teetering between immense admiration and abject terror. My Chatbot does everything for me, from scheduling how to get my revise and resubmit done in three months to planning my wardrobe for the fall semester. I fear becoming too self-reliant on it. Am I losing myself? Am I turning my ChatGPT into a psychological crutch? And if I am having these thoughts, what effect is generative AI having on my students?

Remix vs. Originality: Girl Talk or Beyonce

Grappling with the strengths and weaknesses of my own AI usage, I feel I have discovered what might be the saving grace of humanity (feel free to nominate me for the Nobel Peace Prize if you wish). As I hinted earlier, AI is more like a DJ remixing the greatest hits of society rather than an innovative game changer. My ChatGPT is more like Girl Talk (who you have probably never heard of. Just ask your AI) than Beyonce (who you most definitely have heard of). Not that there’s anything wrong with Girl Talk. Their mashups are amazing and require a special kind of talent. Just like navigating AI usage requires a certain balance of skills to create a usable final product. But no matter how many pieces of music from other artists you mash together, you will not eventually turn into a groundbreaking, innovative musician. Think Pat Boone vs. The Beatles, Sha Na Na vs. David Bowie, Milli Vanilli vs. Prince, MC Hammer vs. Lauryn Hill.

What AI Gets Wrong in Writing

As a mathematician and a novelist, I see this glaring weakness in both of these very different disciplines. I’ll start with writing. ChatGPT is especially helpful in coming up with strange character or planet names for my science fiction novels. It will also help me create a disease or something else I need to drive the plot further. And, of course, it can help me find an errant comma or fix a fragmented sentence. But that is about it. If I ask it to write an entire chapter, for example, it will come up with the most boring, derivative, and bland excuse for prose I have ever seen. It will attempt my humor but fail miserably. It sometimes makes my stomach turn, it’s so bad.

A study from the Wharton School found that ChatGPT reduces the diversity of ideas in a pool of ideas. Thus, it diminishes the diversity of the overall output, narrowing the scope of novel ideas. Beyond that, I find that when I use ChatGPT to brainstorm, I typically don’t use its suggestions. Those suggestions just spark new ideas and help me come up with something different and more me.

For example, I asked ChatGPT to write a joke for its bad brainstorming practice of using the same core ideas over and over again. It said:

Joke: That’s not brainstorming—it’s a lazy mime troupe echoing each other.

That’s lame. I would never say that. But another joke it gave me sparked the music sampling analogy I opened this article with.

In any case, because of generative AI’s inability to actually generate anything new, I have hope that the college essay, like the fiction novel, will not die. Over-reliance on AI may indeed debilitate the essay, perhaps causing it to go on life support forcing students and faculty to drag its lifeless body across the finish line of graduation. But there is still hope.

I remember one of my favorite English teachers in middle school required that we keep a journal. Each day she asked us to write something, anything in our journal, even if it was only a paragraph or just a sentence. Something about putting pen to paper sparked my creativity. It also sparked a lifelong notebook addiction. And even though I consider myself somewhat of a techie and a huge AI enthusiast, to this day I still use notebooks for the first draft of my novels.

It is clear to me that ChatGPT will never be able to write my novels in my voice. I don’t claim to be a great novelist. I just feel that some of my greatest work hasn’t been written yet. While ChatGPT may be able to write a poem about aardvarks in the style of Robert Frost or a ballad about Evariste Galois in the style of Carole King, it can’t write my next novel, because it doesn’t yet exist. And even when it tries to imitate my voice and my style, predicting what I will write next, it does a poor job.

The Research Paper Dilemma: AI vs. Process

A research paper is inherently different from a creative work of fiction, however. ChatGPT does do a pretty good job of gathering information on a topic from several sources and synthesizing it into a coherent paper. You just have to make sure to check for the errant hallucinated reference. And honestly, when are our students ever going to be asked to write a 15-page research paper on Chaucer without any resources? And if they are, ChatGPT can probably produce that product better than an undergraduate student can. But the process, I would argue, is more important than the final product.

In his Inside Higher Ed paper Writing the Research Paper Slowly, JT Torres recommends a scaffolding process to writing the research paper. This method focuses on the process of writing a paper, exploring and reading sources, taking notes, organizing those notes into a ‘scientific story’ and creating an outline. Teaching students the process of writing the paper instead of focusing on the end product results in students feeling more confident that they can not only complete the task required but transfer those skills to another subject. Recognizing these limitations pushed me to rethink how I design assignments.

Using AI in the Classroom

Knowing that generative AI can do somethings (but not all things) better than a human has made me a more intentional professor. Now when I create assignments, I think: Can ChatGPT do this better than an undergraduate student? If so, then what am I really trying to teach? Here are a few strategies I use:

Method #1: Assess Your Assessments with AI in Mind

When designing an assignment, ask yourself whether it is testing a skill that AI already performs well. If so, consider shifting your focus to why that skill matters, or how students can go beyond AI’s capabilities.

Method #2: Use AI Where It Adds Value – Remove It Where It Does Not

In some cases, it makes sense to integrate AI directly into the assignment (e.g., generating code, automating data analysis). In others, the objective may be to build a human-only skill like personal expression or creative voice. I decide case by case whether AI should be a part of the process or explicitly excluded.

Method #3: Clarify Whether You are Teaching Theory or Application

When I am teaching tests, I have to ask myself: Am I assessing whether students understand the theory behind the test or whether they can run one using software? If it’s the latter, using AI to generate code might be appropriate. But if it’s the former, I’ll require manual calculations or a written explanation.

Method #4: Add a Reflection to any AI Supported Assignment

Any assignment where they are allowed to use AI, they also have to write a reflection about how they used AI and whether it was helpful or not. This encourages metacognition and reduces overreliance.

Method #5: Require Students to Share Their Prompts and Revisions

Having students share the prompts they used in completing the assignment teaches them about transparency and the need for iteration in their interaction with an AI. Students should not just be cutting and pasting the first response from ChatGPT. They need to learn how to take a response, analyze it then refine their prompt to get a better result. This helps them develop prompt engineering skills and realize that ChatGPT is not just a magic answer machine.

AI and the Limits of Innovation in Research

What about academic research in general? How is AI helping or hindering? Given that generative AI merely remixes the greatest hits of human history rather than creating anything new, I think its role in academic research is limited. Academic breakthroughs start with unasked questions. Generative AI works within the confines of existing data. It can’t sense the frontier because it doesn’t know there is a frontier. It can’t sample past answers of a question that hasn’t been asked yet. About a year ago, I was trying to get my AI to write a section of code for my research and it kept failing. I spent a week trying to get it to do what I wanted. I realized it was having such a difficult time because I was asking it to do something that hadn’t been done before. Finally, I gave up and wrote the piece of code myself, and it only took me about half an hour. Sure, the coding capabilities have gotten better over the past year, but the core principle remains the same. AI still struggles to innovate. It can’t do what hasn’t already been done. Also because of ‘creative flattery’ it wants to make you happy so it will try to do what you tell it to do even if it can’t. The product will be super convincing, but it can still be wrong.

I recently asked AI to write a theoretical proof that shows polygonal numbers are Benford distributed (Spoiler: They are not). Then I had it help me write a convincing journal-ready article. The only problem is it also wrote me a theoretical proof that Polygonal numbers are NOT Benford distributed as well. I submitted the former to a leading mathematics journal to see what would happen. Guess what, they caught it. A human was able to detect the ‘AI Slop’. This shows me, that (1) there will always be a need for human gatekeepers and (2) ‘creative flattery’ is extremely dangerous in a research setting and confirms the need for human review. The chatbot tries too hard to please, thus reinforcing what the user already thinks even if that means proving or disproving the exact same thing. Academic research thrives on novel questions and unpredictable answers, which AI is incapable of doing since it inherently just regurgitates what is already out there.

Helping Students See AI’s Blind Spots

The Benford Polygonal Numbers experiment is an important example of how we need to educate our students about AI usage in an academic setting. The Time.com article Why A.I. is Getting Less Reliable, Not More states that despite its progress over the years, AI can still resemble sophisticated misinformation machines. Students need to know how to navigate this.

One of my favorite assignments in my Statistics course is what I call:

Method #6: “Beat ChatGPT” – A Concept Mastery Challenge

Students must craft a statistics question that the chatbot gets wrong, explain why the chatbot got it wrong and then provide the correct answer. A tweak of this activity would be to take AI generated content and human written then compare and critique tone, clarity, or originality.

Remixing isn’t Creation

AI-generated content is like a song built entirely from remixed samples. Sampling has its place in music (and in writing) but when everything starts to sound the same, our ears and brains begin to tune out. A great remix can breathe new life into a classic, but we still crave the shock of the new. This is why people lost their minds the first time they heard Beyonce’s Lemonade or Kendrick Lamar’s To Pimp a Butterfly – not because they followed a formula, but because they bent the rules and made something we’d never heard before. AI, for all its value, doesn’t break the rules. It follows them. That is the difference between innovation and imitation. It is also the reason why AI, in its current capacity, will not kill original thought.

Sybil Prince Nelson, PhD, is an assistant professor of mathematics and data science at Washington and Lee University, where she also serves as the institution’s inaugural AI Fellow. She holds a PhD in Biostatistics and has over two decades of teaching experience at both the high school and college levels. She is also a published fiction author under the names Sybil Nelson and Leslie DuBois.

References

Hsu, Hua. 2025. “The End of the English Paper.” The New Yorker, July 7, 2025. https://www.newyorker.com/magazine/2025/07/07/the-end-of-the-english-paper.

Warner, John. 2024. “Get Ready for Faculty Bot-ification.” Inside Higher Ed, December 11, 2024. https://www.insidehighered.com/opinion/columns/just-visiting/2024/12/11/great-ready-faculty-bot-ification.

Meincke, Lea, Gideon Nave, and Christian Terwiesch. 2025. “ChatGPT Decreases Idea Diversity in Brainstorming.” Nature Human Behaviour 9: 1107–1109. https://doi.org/10.1038/s41562-025-02173-x.

Torres, J. T. 2021. “Writing the Research Paper Slowly.” Inside Higher Ed, May 5, 2021. https://www.insidehighered.com/advice/2021/05/05/benefits-new-approach-student-research-papers-opinion.

Sonnenfeld, Jeffrey, and Joanne Lipman. 2024. “Why A.I. Is Getting Less Reliable, Not More.” Time, June 20, 2024. https://time.com/7302830/why-ai-is-getting-less-reliable/.

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October 29, 2025
Unlocking Learning Potential with Concept Maps – Faculty Focus
If you’ve ever tried to make sense of a big, messy topic, whether it’s anatomy, psychology, or even planning a family vacation, you know how easy it is to get lost in the details. That’s where concept maps come in.

A concept map is more than just a fancy diagram. It’s a visual tool that helps you capture, organize, and connect ideas so you can see the “big picture” at a glance.

Why Concept Maps Work

Concept maps tap into how our brains naturally work, by connecting information rather than memorizing isolated facts. When you create one, you’re not just copying notes; you’re actively making sense of information, spotting patterns, and building links between concepts.

This active engagement is backed by research: visual representations improve comprehension, support critical thinking, and boost retention. In short, concept maps make learning stick. Nesbit and Adesope’s (2006) meta-analysis of dozens of studies found that students who used concept maps showed significantly stronger learning outcomes compared to those who relied only on traditional notetaking.

Cognitive psychology suggests that learning is strongest when it is meaningful—when new knowledge connects to what we already know. Concept maps encourage this process by forcing us to ask, how does this idea relate to that one? That act of questioning builds deeper understanding and promotes long-term memory. Novak and Cañas (2008), pioneers in concept mapping, argue that these tools “make thinking visible” by transforming abstract knowledge into something concrete and structured.

How to Build a Concept Map
1. Start with a central idea. Write it in the center or top of your page.
1. Branch out. Add related subtopics, drawing lines or arrows to show relationships.
1. Label your connections. These labels (like “causes,” “leads to,” “is a type of”) turn your map into a meaningful web rather than a random cluster of words.
1. Add details. Keep branching until you’ve covered all major points and their relationships.
1. Review and refine. Your first map is rarely your last. Adjust as you deepen your understanding.
The beauty of concept maps is that they are highly adaptable. Some people prefer a simple structure with just a few layers, while others create elaborate webs with cross-links between multiple concepts. There’s no single “right” way to build one. The key is whether it helps you make sense of the material.

Real-World Uses for Concept Maps

Concept maps shine in both academic and professional settings:
- Students use them to study complex topics, such as linking symptoms, diagnoses, and treatments in a medical course. This not only aids exam prep but also mirrors how professionals think in the real world.
- Educators design them to structure lessons, showing how each concept builds on the previous one. Concept maps also work as formative assessments. Teachers can quickly see what students understand and where misconceptions remain.
- Professionals apply them in project planning, research design, and problem-solving. A well-structured map can turn a vague idea into a clear, actionable plan.
In healthcare education, for example, concept maps are widely used in nursing programs to help students link theory to practice. By mapping patient symptoms to interventions and outcomes, learners demonstrate both clinical reasoning and holistic thinking. In business, managers use concept maps to brainstorm strategies, identify risks, and visualize workflows before moving into detailed planning. Even in everyday life, families use concept maps to coordinate large events like weddings or vacations—organizing everything from budgets to timelines at a glance.

Going Beyond the Basics

Once you’ve mastered the fundamentals, concept maps can become even more powerful with advanced techniques:
- Cross-links: Draw connections between branches that may not seem directly related. These links often spark “aha” moments by revealing hidden relationships.
- Hierarchy: Organize ideas from the most general at the top to the most specific at the bottom, which helps clarify levels of abstraction.
- Integration with technology: Digital concept mapping tools allow for collaboration, multimedia integration, and real-time updates, making them ideal for group projects or remote learning environments.
Teachers can also use concept maps to measure growth over time. For example, students might create a map at the start of a unit and then expand or revise it at the end. Comparing the two versions provides a visible record of learning progress, something that’s harder to capture with multiple-choice tests alone.

Pro Tips for Making Your Map Effective
- Use color coding to group related ideas.
- Keep your wording short—think keywords, not paragraphs.
- Experiment with digital tools like Coggle, MindMeister, or Lucidchart for easy editing and sharing.
- Don’t be afraid to collaborate. Building a map with classmates or colleagues can reveal insights you might have missed on your own.
The Takeaway

Concept maps aren’t just about making pretty diagrams; they’re about making thinking visible. Whether you’re a student, teacher, or lifelong learner, this tool can transform the way you process and remember information.

So next time you’re overwhelmed by a new topic, skip the endless bullet points and map it out. You might be surprised at how much clearer everything becomes.

Michele Poulos has dedicated more than twenty-five years to education, with experience spanning elementary, secondary, and post-secondary settings. She holds a Bachelor’s degree in Elementary Education, a Master’s degree in Psychology, and is currently pursuing her doctoral degree in Human and Learning Science. Throughout her career, she has taught and learned in both traditional classrooms and fully online environments, giving her a unique perspective on how students thrive across modalities. Michele currently serves as the Director of Online Education at Pima Medical Institute, where she oversees online programs, faculty development, and strategic initiatives designed to enhance student learning and success. Her professional accomplishments have been recognized nationally, as she was inducted into Marquis Who’s Who in America for four consecutive years (2022–2025). Her passion for education is matched by her commitment to family life. She resides in Naples, Florida, with her husband and their two children—ten-year-old Trenton and eight-year-old Eliana—who continually inspire her dedication to both teaching and lifelong learning.

References

Nesbit, J. C., & Adesope, O. O. (2006). Learning with concept and knowledge maps: A meta-analysis. Review of Educational Research, 76(3), 413–448. https://doi.org/10.3102/00346543076003413

Novak, J. D., & Cañas, A. J. (2008). The theory underlying concept maps and how to construct and use them. Institute for Human and Machine Cognition. Retrieved from http://cmap.ihmc.us/Publications/ResearchPapers/TheoryUnderlyingConceptMaps.pdf
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October 24, 2025
Unlocking Learning Potential with Concept Maps – Faculty Focus
If you’ve ever tried to make sense of a big, messy topic, whether it’s anatomy, psychology, or even planning a family vacation, you know how easy it is to get lost in the details. That’s where concept maps come in.

A concept map is more than just a fancy diagram. It’s a visual tool that helps you capture, organize, and connect ideas so you can see the “big picture” at a glance.

Why Concept Maps Work

Concept maps tap into how our brains naturally work, by connecting information rather than memorizing isolated facts. When you create one, you’re not just copying notes; you’re actively making sense of information, spotting patterns, and building links between concepts.

This active engagement is backed by research: visual representations improve comprehension, support critical thinking, and boost retention. In short, concept maps make learning stick. Nesbit and Adesope’s (2006) meta-analysis of dozens of studies found that students who used concept maps showed significantly stronger learning outcomes compared to those who relied only on traditional notetaking.

Cognitive psychology suggests that learning is strongest when it is meaningful—when new knowledge connects to what we already know. Concept maps encourage this process by forcing us to ask, how does this idea relate to that one? That act of questioning builds deeper understanding and promotes long-term memory. Novak and Cañas (2008), pioneers in concept mapping, argue that these tools “make thinking visible” by transforming abstract knowledge into something concrete and structured.

How to Build a Concept Map
1. Start with a central idea. Write it in the center or top of your page.
1. Branch out. Add related subtopics, drawing lines or arrows to show relationships.
1. Label your connections. These labels (like “causes,” “leads to,” “is a type of”) turn your map into a meaningful web rather than a random cluster of words.
1. Add details. Keep branching until you’ve covered all major points and their relationships.
1. Review and refine. Your first map is rarely your last. Adjust as you deepen your understanding.
The beauty of concept maps is that they are highly adaptable. Some people prefer a simple structure with just a few layers, while others create elaborate webs with cross-links between multiple concepts. There’s no single “right” way to build one. The key is whether it helps you make sense of the material.

Real-World Uses for Concept Maps

Concept maps shine in both academic and professional settings:
- Students use them to study complex topics, such as linking symptoms, diagnoses, and treatments in a medical course. This not only aids exam prep but also mirrors how professionals think in the real world.
- Educators design them to structure lessons, showing how each concept builds on the previous one. Concept maps also work as formative assessments. Teachers can quickly see what students understand and where misconceptions remain.
- Professionals apply them in project planning, research design, and problem-solving. A well-structured map can turn a vague idea into a clear, actionable plan.
In healthcare education, for example, concept maps are widely used in nursing programs to help students link theory to practice. By mapping patient symptoms to interventions and outcomes, learners demonstrate both clinical reasoning and holistic thinking. In business, managers use concept maps to brainstorm strategies, identify risks, and visualize workflows before moving into detailed planning. Even in everyday life, families use concept maps to coordinate large events like weddings or vacations—organizing everything from budgets to timelines at a glance.

Going Beyond the Basics

Once you’ve mastered the fundamentals, concept maps can become even more powerful with advanced techniques:
- Cross-links: Draw connections between branches that may not seem directly related. These links often spark “aha” moments by revealing hidden relationships.
- Hierarchy: Organize ideas from the most general at the top to the most specific at the bottom, which helps clarify levels of abstraction.
- Integration with technology: Digital concept mapping tools allow for collaboration, multimedia integration, and real-time updates, making them ideal for group projects or remote learning environments.
Teachers can also use concept maps to measure growth over time. For example, students might create a map at the start of a unit and then expand or revise it at the end. Comparing the two versions provides a visible record of learning progress, something that’s harder to capture with multiple-choice tests alone.

Pro Tips for Making Your Map Effective
- Use color coding to group related ideas.
- Keep your wording short—think keywords, not paragraphs.
- Experiment with digital tools like Coggle, MindMeister, or Lucidchart for easy editing and sharing.
- Don’t be afraid to collaborate. Building a map with classmates or colleagues can reveal insights you might have missed on your own.
The Takeaway

Concept maps aren’t just about making pretty diagrams; they’re about making thinking visible. Whether you’re a student, teacher, or lifelong learner, this tool can transform the way you process and remember information.

So next time you’re overwhelmed by a new topic, skip the endless bullet points and map it out. You might be surprised at how much clearer everything becomes.

Michele Poulos has dedicated more than twenty-five years to education, with experience spanning elementary, secondary, and post-secondary settings. She holds a Bachelor’s degree in Elementary Education, a Master’s degree in Psychology, and is currently pursuing her doctoral degree in Human and Learning Science. Throughout her career, she has taught and learned in both traditional classrooms and fully online environments, giving her a unique perspective on how students thrive across modalities. Michele currently serves as the Director of Online Education at Pima Medical Institute, where she oversees online programs, faculty development, and strategic initiatives designed to enhance student learning and success. Her professional accomplishments have been recognized nationally, as she was inducted into Marquis Who’s Who in America for four consecutive years (2022–2025). Her passion for education is matched by her commitment to family life. She resides in Naples, Florida, with her husband and their two children—ten-year-old Trenton and eight-year-old Eliana—who continually inspire her dedication to both teaching and lifelong learning.

References

Nesbit, J. C., & Adesope, O. O. (2006). Learning with concept and knowledge maps: A meta-analysis. Review of Educational Research, 76(3), 413–448. https://doi.org/10.3102/00346543076003413

Novak, J. D., & Cañas, A. J. (2008). The theory underlying concept maps and how to construct and use them. Institute for Human and Machine Cognition. Retrieved from http://cmap.ihmc.us/Publications/ResearchPapers/TheoryUnderlyingConceptMaps.pdf
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October 24, 2025
Beyond the First Response: Prompting with Purpose in University Classrooms – Faculty Focus
Are you one of the reported 61% of higher education faculty now using AI in your teaching (Weaver, 2025)? A recent survey by the Digital Education Council (2025) found that 88% of AI-using faculty report minimal to moderate use. Further, 83% of faculty question students’ ability to evaluate AI-generated content, and 82% worry about student overreliance on AI tools.

So, while a majority of faculty are incorporating AI, many of us remain cautious about how to use it effectively in our higher education classrooms. This tension is further echoed in a recent 2025 EDUCAUSE AI Landscape Study, which reports 57% of schools, up from 49% last year, now identify AI as a “strategic priority” as they continue to adapt to the expanding impact of AI across teaching and learning (Robert & McCormack, 2025).

Our institutions want us to use AI in our classrooms, but how can we do this well? Research by Zhou and Peng (2025) found that AI supported instruction can enhance both student engagement and creativity, especially in creating personalized and collaborative learning experiences. Similarly, Walter (2024) found that training educators and students in prompt engineering and critical evaluation is a key component necessary to maximize AI’s potential while reducing risks of misuse and over reliance. To enhance our content, we need to think about how to use AI purposefully, training both ourselves and our students to engage with AI tools critically, creatively, and ethically.

This article examines how faculty can incorporate AI tools effectively into their disciplines, while guiding students to use AI to foster critical thinking and creative application. Drawing on my own research, it offers strategies to support thoughtful integration of AI into higher education classrooms, with a focus on ethical awareness and responsive instruction design.

What I Learned From Using AI in My Teaching

Over the past school year, I used AI as a tool in my undergraduate courses and found that students were not as adept at using AI as I had suspected. In fact, when I introduced AI as a required component of the course framework at the start of the semester, many students were uncertain how to proceed. Some shyly offered that they had used AI in courses previously, but many were hesitant, having been repeatedly warned that using AI could jeopardize their academic careers. Without explicit, scaffolded instruction, both students and faculty risk using AI superficially, missing its potential to meaningfully transform teaching and learning.

When AI Becomes the Assignment

In Spring 2025, I led a research project in my classes exploring how university students used AI tools, such as ChatGPT, to support iterative writing and refining complex tasks like lesson planning. The study emphasized ethical AI use and focused on prompt engineering techniques, including the use of voice-to-text, targeted revision, and staged feedback loops to improve idea generation, structure, and differentiation. I wanted students to engage in a critical evaluation of AI outputs, developing greater precision and agency in applying AI suggestions across drafting stages.

What I found was that students did not initially know how to talk to AI, rather they talked at it. At first, students did not get useful results because they were not tailoring their prompts enough. One student offered “I had to ask the same question 50 billion different ways to get the right answer.” What I discovered over those first few weeks was that students needed to learn to dialogue with AI in the right ways. They had to be intentional in what they were asking it and tailor their prompts specifically.

Try this instead:
- Begin broad, then refine. Encourage students to start with a general idea, then narrow their prompts based on assignment goals and relevance of the AI’s output.
- Promote iterative prompting. Teach students to revise their prompts by engaging in an ongoing process of dialoguing with AI, aimed at narrowing down their ideas. Author WonLee Hee (2025) offers the following framework: prompt, generate output, analyze, refine prompt, and repeat.
Why Prompting Is Worth Teaching

Students are using AI, but often without the skills to do so effectively—and that is where we come in. Poor prompting reinforces the very over-reliance that faculty fear, training students to accept whatever results AI delivers, rather than critically questioning them. When prompts are vague or generic, the results are too.

Students need specific instruction on how to prompt AI effectively. In my classes I used a structured, multi-step process that students followed each week. However, after reviewing student feedback and surveys, I realized that the process involved too many steps. If I wanted my students to use AI meaningfully beyond my course, I would need to refine and simplify the approach.

Try this instead:
- Incorporate guided practice. Use a consistent AI tool at the start of the semester (I used ChatGPT) and model effective prompting and revision to help students build foundational skills.
- Gradually increase student choice. After the initial learning phase, allow students to mix and match AI tools to personalize the process and deepen their engagement.
- Embed critical reflection. Encourage students to treat AI as a thinking partner, not an all-knowing source. Design assignments so that they require ongoing interaction with AI (Gonsalves, 2024), such as using AI to generate counterarguments to their own essays or applying math concepts to real-world problems to identify gaps or misunderstandings in their thinking.
A Simple Framework for Better Prompts

A simple, three-phased framework will be more user friendly.
1. Explore: Encourage students to begin by collecting and thinking through wide-ranging ideas. Start with speech-to-text to brainstorm. Then narrow the focus, identify gaps, and use AI to help fill them.
1. Refine: Have students evaluate the AI outputs and add specific details to further improve clarity, accuracy, and relevance.
1. Revise: Use AI to check if ideas have been clearly communicated. This type of editing involves more than fixing grammar, it is about making sure that their message is clear, focused, and appropriate for the audience.
What Changed for Students

When I incorporated these changes, I saw that my students became more strategic thinkers and were less likely to merely copy from AI. In fact, over 73% of my study participants noted that they stopped accepting AI’s first response and began asking better follow-up questions, indicating that they were dialoguing with AI rather than just copying from it. Repeated practice helped them yield more accurate AI generated support and emphasized their importance in the process. They came to view AI as a support tool not a substitute for their own ideas. At the end of the study, one student noted “You have to be very specific… I have learned how to tweak my prompt to get the result I want.” Another, stated that “I started editing ChatGPT instead of letting it write for me.” These responses indicated a key shift: better prompting had reframed AI as a collaborator, not a crutch.

Final Thoughts

Teaching students how to create effective prompts is not about using technology, it is about teaching them to craft better questions. This practice reinforces critical thinking skills so many of us aim to develop in our disciplines. When students learn how to guide AI, they are also learning how to refine their own thinking. Encouraging reflection throughout the process fosters metacognition; by regularly engaging in this type of analysis of their decisions and ideas, students become more thoughtful, independent learners. By intentionally incorporating AI tools into our coursework, we are reducing the temptation for misuse and overreliance, creating space for more ethical and transparent use in our higher education classrooms.

AI Disclosure: This article reflects collaboration between the human author and OpenAI’s ChatGPT-4 for light editing. All ideas, examples, and interpretations are the author’s own.

Lisa Delgado Brown, PhD, is a current Assistant Professor of Education at The University of Tampa and the former Middle/Secondary Program Administrator at Saint Leo University where she also served on the Academic Standards Committee. Dr. Delgado Brown teaches literacy courses with a focus on differentiation in the general education classroom.

References

Gonsalves, C. (2024). Generative AI’s Impact on Critical Thinking: Revisiting Bloom’s Taxonomy. Journal of Marketing Education, 0(0). https://doi.org/10.1177/02734753241305980

Lee, W. (2025, April 29). Prompt engineering #5: Optimizing AI interactions through iterative prompt adjustments. Medium. https://medium.com/@whee.2013/optimizing-ai-interactions-through-iterative-prompt-adjustments-9ec0974ee821

Robert, J., & McCormack, M. (2025, February 17). 2025 EDUCAUSE AI landscape study: Into the digital AI divide. EDUCAUSE. https://www.educause.edu/content/2025/2025-educause-ai-landscape-study/introduction-and-key-findings

Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21, 15. https://doi.org/10.1186/s41239-024-00448-3

Weaver, J. (2025, February 5). Digital Education Council: Global AI meets academia faculty survey 2025. IBL Education. https://ibl.ai/blog?story=digital-education-council-global-ai-meets-academia-faculty-survey-2025

Zhou, M., & Peng, S. (2025). The usage of AI in teaching and students’ creativity: The mediating role of learning engagement and the moderating role of AI literacy. Behavioral Sciences, 15(5), 587. https://doi.org/10.3390/bs15050587
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September 29, 2025
The Collaborative AI Classroom: Teaching Students to Work With, Not Against, AI Tools – Faculty Focus
In today’s rapidly evolving educational landscape, artificial intelligence (AI) has transformed from a distant technological concept to an everyday tool accessible to our students. In recent years, AI has proven to be a disruptor in the delivery of education, raising concerns among educators. Rather than viewing AI as a threat to academic integrity or a shortcut that undermines learning, educators have an opportunity to reframe AI as a collaborative partner in the educational process (Bowen and Watson, 2024). This article examines practical approaches for teaching students to collaborate with AI tools in a manner that enhances, rather than replaces, critical thinking and deep learning.

The Collaborative Mindset Shift

According to the Global AI Student Survey by the Digital Education Council, 86% of students already use AI in their studies (“Digital Education Council Global AI Student Survey 2024,” n.d.). However, with widespread apprehension about the effects of AI in education, a mindset shift may be beneficial in alleviating fears of the unknown. The first step in creating a collaborative AI classroom is to shift both the instructor’s and student’s mindsets. As Bowen and Watson (2024) note in their book, Teaching with AI, ‘AI is going to change our relationship with thinking. It is already challenging ideas about creativity and originality, and it will forever alter education, work, and even how we think about thinking.’ They further posit that students and educators can work with generative AI systems to improve learning processes, making them faster and more efficient. They propose that assessments should shift to focus on the process of creation rather than just the final product. For instance, students might be required to submit transcripts of their AI interactions as part of their assignments, making the learning journey more transparent and accessible.

This approach aligns with Atchley et al. (2024), who conclude that while the new AI technologies in education will initially make educators’ jobs challenging, AI should be viewed as a collaborative tool for teachers and students alike. Their review examines the adoption of AI in education from the perspective that the primary outcome of higher education is employment, that education is centered around the cognitive domains associated with learning, and that there is a risk associated with allowing technology to perform cognitive tasks. Keeping those factors in mind, they posit that ‘to maximize the benefits of collaborative learning (between fully human teams and between teams that include AI), pedagogical strategies must intentionally incorporate primary and secondary factors that encourage shared responsibility, interaction, and metacognitive skill development, enhancing student engagement and learning outcomes.’ Further, they argue that ‘as students enter a workplace, their ability to work on human/computer “teams” is a critical cognitive skillset.’

Practical Classroom Strategies

1. AI-Assisted Drafting and Revision

Instead of banning AI writing tools, consider assignments where students deliberately use AI to generate initial drafts, then critically analyze and substantially revise those drafts. This process teaches students to:
- Craft effective prompts (a transferable skill)
- Identify strengths and weaknesses in AI-generated content
- Apply their own expertise and voice to improve upon machine outputs
Students can submit both the original AI output and their revised version, along with a reflection on their editing process. In addition, asking students to support their edits with evidence-based references teaches them to critically analyze AI outputs. This transparency transforms potential academic dishonesty into a learning opportunity about the value of human expertise and judgment.

2. Comparative Analysis Activities

Design activities where students compare multiple AI-generated responses to the same prompt and then evaluate which is most effective and why. This approach:
- Develops critical evaluation skills
- Demonstrates AI limitations and inconsistencies
- Reinforces that AI requires human oversight
For example, students might prompt three different AI tools with the same question and then analyze differences in factual accuracy, reasoning approaches, and potential biases.

3. AI as Research Assistant

Teach students to use AI as a research brainstorming tool while maintaining scholarly rigor. Students can:
- Use AI to generate potential research questions
- Identify gaps in AI knowledge that require traditional research
- Cross-check AI-suggested sources with academic databases
This approach, similar to what Cianciolo and Regehr (2019) describe as “layered analysis,” helps students understand the complementary relationship between AI-assisted and traditional research methods.

Evidence of Impact

Early evidence suggests that collaborative AI approaches improve learning outcomes. In a study of healthcare education environments, students who learned to critically engage with AI tools, such as simulated gaming (SG), expressed more satisfaction with the training session than the traditional teaching group (TT). Additionally, students in the SG group found the activity more engaging and reported increased motivation (Blanie et al., 2020).

Similarly, in a recent meta-analysis examining the relationship between AI and students’ academic achievement, Dong et al. (2025) concluded that students who utilized AI-enhanced learning approaches demonstrated significantly higher academic performance, outpacing their counterparts in conventional educational settings. Moreover, the authors concluded that AI technologies may be particularly valuable for secondary and higher education students, who typically possess more developed critical thinking skills and can engage meaningfully with sophisticated AI systems that may apply across disciplines.

Implementation Considerations

When implementing collaborative AI approaches, consider these guidelines:
1. Be explicit about when and how AI use is appropriate for specific assignments
1. Create rubrics that evaluate students’ critical engagement with AI, not just the final products
1. Model appropriate AI collaboration in your teaching practices
1. Provide scaffolded opportunities for students to practice AI collaboration with feedback
Conclusion

By teaching students to work with, rather than against, AI tools, we prepare them for a future where human-AI collaboration will be commonplace across various professions. The collaborative AI classroom doesn’t diminish the value of human thinking, instead, it elevates it by helping students understand what uniquely human perspectives they bring to problems that AI alone cannot solve.

Rather than focusing solely on outcomes (what students produce), this approach emphasizes the learning process itself, aligning with calls from Allen et al. (2021) for more comprehensive evaluation approaches in education that go beyond simple outcome measures. By teaching students to be thoughtful collaborators with AI, we help them develop the critical thinking skills and technological literacy they’ll need throughout their careers.

Ewa Posorski is an educational leader and advocate for integrating artificial intelligence into modern pedagogy. With a focus on fostering collaboration between students and AI tools, Ewa champions a shift in mindset that embraces AI as a partner in learning rather than a threat to academic integrity. Her work emphasizes practical strategies for AI-assisted drafting, comparative analysis, and research support, all aimed at enhancing critical thinking and metacognitive skills. Drawing on current research and classroom evidence, she promotes transparent, process-oriented learning that prepares students for a future of human-AI collaboration across disciplines.

References

Allen, L. M., Hay, M., & Palermo, C. (2021). Evaluation in health professions education—Is measuring outcomes enough? Medical Education, 56(1), 127-136. https://doi.org/10.1111/medu.14654

Atchley, P., Pannell, H., Wofford, K., Hacker, D. J., & Risko, E. F. (2024). Human and AI collaboration in the higher education environment: opportunities and concerns. Cognitive Research: Principles and Implications, 9, 20. https://doi.org/10.1186/s41235-024-00547-9

Blanié, A., Amorim, M. A., & Benhamou, D. (2020). Comparative value of simulation by gaming and a traditional teaching method to improve clinical reasoning skills necessary to detect patient deterioration: A randomized study in nursing students. BMC Medical Education, 20, 53. https://doi.org/10.1186/s12909-020-1939-6

Bowen, J.A. & Watson, C.E. (2024). Teaching with AI: A practical guide to a new era of human learning. Johns Hopkins University Press.

Cianciolo, A. T., & Regehr, G. (2019). Learning Theory and Educational Intervention: Producing Meaningful Evidence of Impact Through Layered Analysis. Academic Medicine: Journal of the Association of American Medical Colleges, 94(6), 789–794. https://doi.org/10.1097/ACM.0000000000002591

Bowen, José Antonio, and C. Edward Watson. 2024. Teaching with AI: A Practical Guide to a New Era of Human Learning. Baltimore, Maryland: Johns Hopkins University Press.

Digital Education Council. “Digital Education Council Global AI Student Survey 2024.” n.d. Accessed March 24, 2025. https://www.digitaleducationcouncil.com/post/digital-education-council-global-ai-student-survey-2024.

Liu, D., Tang, X., & Wang, X. (2025). Examining the effect of artificial intelligence in relation to students’ academic achievement: A meta-analysis. Computers and Education: Artificial Intelligence. https://www.sciencedirect.com/science/article/pii/S2666920X25000402
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August 1, 2025
The Collaborative AI Classroom: Teaching Students to Work With, Not Against, AI Tools – Faculty Focus
In today’s rapidly evolving educational landscape, artificial intelligence (AI) has transformed from a distant technological concept to an everyday tool accessible to our students. In recent years, AI has proven to be a disruptor in the delivery of education, raising concerns among educators. Rather than viewing AI as a threat to academic integrity or a shortcut that undermines learning, educators have an opportunity to reframe AI as a collaborative partner in the educational process (Bowen and Watson, 2024). This article examines practical approaches for teaching students to collaborate with AI tools in a manner that enhances, rather than replaces, critical thinking and deep learning.

The Collaborative Mindset Shift

According to the Global AI Student Survey by the Digital Education Council, 86% of students already use AI in their studies (“Digital Education Council Global AI Student Survey 2024,” n.d.). However, with widespread apprehension about the effects of AI in education, a mindset shift may be beneficial in alleviating fears of the unknown. The first step in creating a collaborative AI classroom is to shift both the instructor’s and student’s mindsets. As Bowen and Watson (2024) note in their book, Teaching with AI, ‘AI is going to change our relationship with thinking. It is already challenging ideas about creativity and originality, and it will forever alter education, work, and even how we think about thinking.’ They further posit that students and educators can work with generative AI systems to improve learning processes, making them faster and more efficient. They propose that assessments should shift to focus on the process of creation rather than just the final product. For instance, students might be required to submit transcripts of their AI interactions as part of their assignments, making the learning journey more transparent and accessible.

This approach aligns with Atchley et al. (2024), who conclude that while the new AI technologies in education will initially make educators’ jobs challenging, AI should be viewed as a collaborative tool for teachers and students alike. Their review examines the adoption of AI in education from the perspective that the primary outcome of higher education is employment, that education is centered around the cognitive domains associated with learning, and that there is a risk associated with allowing technology to perform cognitive tasks. Keeping those factors in mind, they posit that ‘to maximize the benefits of collaborative learning (between fully human teams and between teams that include AI), pedagogical strategies must intentionally incorporate primary and secondary factors that encourage shared responsibility, interaction, and metacognitive skill development, enhancing student engagement and learning outcomes.’ Further, they argue that ‘as students enter a workplace, their ability to work on human/computer “teams” is a critical cognitive skillset.’

Practical Classroom Strategies

1. AI-Assisted Drafting and Revision

Instead of banning AI writing tools, consider assignments where students deliberately use AI to generate initial drafts, then critically analyze and substantially revise those drafts. This process teaches students to:
- Craft effective prompts (a transferable skill)
- Identify strengths and weaknesses in AI-generated content
- Apply their own expertise and voice to improve upon machine outputs
Students can submit both the original AI output and their revised version, along with a reflection on their editing process. In addition, asking students to support their edits with evidence-based references teaches them to critically analyze AI outputs. This transparency transforms potential academic dishonesty into a learning opportunity about the value of human expertise and judgment.

2. Comparative Analysis Activities

Design activities where students compare multiple AI-generated responses to the same prompt and then evaluate which is most effective and why. This approach:
- Develops critical evaluation skills
- Demonstrates AI limitations and inconsistencies
- Reinforces that AI requires human oversight
For example, students might prompt three different AI tools with the same question and then analyze differences in factual accuracy, reasoning approaches, and potential biases.

3. AI as Research Assistant

Teach students to use AI as a research brainstorming tool while maintaining scholarly rigor. Students can:
- Use AI to generate potential research questions
- Identify gaps in AI knowledge that require traditional research
- Cross-check AI-suggested sources with academic databases
This approach, similar to what Cianciolo and Regehr (2019) describe as “layered analysis,” helps students understand the complementary relationship between AI-assisted and traditional research methods.

Evidence of Impact

Early evidence suggests that collaborative AI approaches improve learning outcomes. In a study of healthcare education environments, students who learned to critically engage with AI tools, such as simulated gaming (SG), expressed more satisfaction with the training session than the traditional teaching group (TT). Additionally, students in the SG group found the activity more engaging and reported increased motivation (Blanie et al., 2020).

Similarly, in a recent meta-analysis examining the relationship between AI and students’ academic achievement, Dong et al. (2025) concluded that students who utilized AI-enhanced learning approaches demonstrated significantly higher academic performance, outpacing their counterparts in conventional educational settings. Moreover, the authors concluded that AI technologies may be particularly valuable for secondary and higher education students, who typically possess more developed critical thinking skills and can engage meaningfully with sophisticated AI systems that may apply across disciplines.

Implementation Considerations

When implementing collaborative AI approaches, consider these guidelines:
1. Be explicit about when and how AI use is appropriate for specific assignments
1. Create rubrics that evaluate students’ critical engagement with AI, not just the final products
1. Model appropriate AI collaboration in your teaching practices
1. Provide scaffolded opportunities for students to practice AI collaboration with feedback
Conclusion

By teaching students to work with, rather than against, AI tools, we prepare them for a future where human-AI collaboration will be commonplace across various professions. The collaborative AI classroom doesn’t diminish the value of human thinking, instead, it elevates it by helping students understand what uniquely human perspectives they bring to problems that AI alone cannot solve.

Rather than focusing solely on outcomes (what students produce), this approach emphasizes the learning process itself, aligning with calls from Allen et al. (2021) for more comprehensive evaluation approaches in education that go beyond simple outcome measures. By teaching students to be thoughtful collaborators with AI, we help them develop the critical thinking skills and technological literacy they’ll need throughout their careers.

Ewa Posorski is an educational leader and advocate for integrating artificial intelligence into modern pedagogy. With a focus on fostering collaboration between students and AI tools, Ewa champions a shift in mindset that embraces AI as a partner in learning rather than a threat to academic integrity. Her work emphasizes practical strategies for AI-assisted drafting, comparative analysis, and research support, all aimed at enhancing critical thinking and metacognitive skills. Drawing on current research and classroom evidence, she promotes transparent, process-oriented learning that prepares students for a future of human-AI collaboration across disciplines.

References

Allen, L. M., Hay, M., & Palermo, C. (2021). Evaluation in health professions education—Is measuring outcomes enough? Medical Education, 56(1), 127-136. https://doi.org/10.1111/medu.14654

Atchley, P., Pannell, H., Wofford, K., Hacker, D. J., & Risko, E. F. (2024). Human and AI collaboration in the higher education environment: opportunities and concerns. Cognitive Research: Principles and Implications, 9, 20. https://doi.org/10.1186/s41235-024-00547-9

Blanié, A., Amorim, M. A., & Benhamou, D. (2020). Comparative value of simulation by gaming and a traditional teaching method to improve clinical reasoning skills necessary to detect patient deterioration: A randomized study in nursing students. BMC Medical Education, 20, 53. https://doi.org/10.1186/s12909-020-1939-6

Bowen, J.A. & Watson, C.E. (2024). Teaching with AI: A practical guide to a new era of human learning. Johns Hopkins University Press.

Cianciolo, A. T., & Regehr, G. (2019). Learning Theory and Educational Intervention: Producing Meaningful Evidence of Impact Through Layered Analysis. Academic Medicine: Journal of the Association of American Medical Colleges, 94(6), 789–794. https://doi.org/10.1097/ACM.0000000000002591

Bowen, José Antonio, and C. Edward Watson. 2024. Teaching with AI: A Practical Guide to a New Era of Human Learning. Baltimore, Maryland: Johns Hopkins University Press.

Digital Education Council. “Digital Education Council Global AI Student Survey 2024.” n.d. Accessed March 24, 2025. https://www.digitaleducationcouncil.com/post/digital-education-council-global-ai-student-survey-2024.

Liu, D., Tang, X., & Wang, X. (2025). Examining the effect of artificial intelligence in relation to students’ academic achievement: A meta-analysis. Computers and Education: Artificial Intelligence. https://www.sciencedirect.com/science/article/pii/S2666920X25000402
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August 1, 2025
The Art of Collaboration: Designing Assignments That Work – Faculty Focus
Inclusivity and feelings of psychological safety in the classroom should not be reserved solely for K-12 learning environments. Students in higher education also deserve nurturing spaces that focus on utilizing their personalized strengths and needs to foster increased academic and social development. A costly misconception is that pedagogical approaches in higher education must be lecture-based and teacher-led. Instead, it is an educator’s responsibility to design cooperative learning structures that facilitate comfort and collaboration regardless of grade level. These practical techniques will develop critical thinking and problem-solving abilities by creating intentional opportunities to enhance social and communication skills. The fundamental question becomes: How can educators plan for and deliver instructional content that is relatable and meaningful?

According to a recent survey by Salesforce (2022), just 12% of college students felt a strong sense of belonging in their institutions. Feelings of disconnection from their peers and institutions persisted after students returned to campus from remote learning during the COVID-19 pandemic. To address this dismal response to education, we must promote inclusive and respectful learning environments that foster engagement, motivation, and active discourse.

Purposeful Application to Teamwork

A practical and successful strategy is to design performance tasks using cooperative learning structures deliberately. Aside from promoting positive interdependence and individual accountability, students participate equally through simultaneous interactions. These actionable, collaborative strategies help educators navigate their role as facilitators, guiding and responding rather than lecturing and directing.

In an undergraduate education course, I establish a positive tone by holding morning meetings at the start of every class. The intention is to incorporate short, structured routines that foster community, address social-emotional needs, and transition students to the day’s lesson. Purposefully, I provide opportunities for students to connect and practice the social skills necessary to participate in rigorous critical thinking and problem-solving tasks. The goal is to elicit positive peer interactions and foster a sense of belonging and trust where students consider multiple perspectives and actively engage in an inclusive and receptive learning environment.

To enrich their classroom experience, I align cooperative learning structures with the lesson outcomes, ensuring that 21st-century, real-world content tasks are purposeful and authentic. Motivated by intentional opportunities for collaboration, students actively process information in favorable conditions that are both supportive and stimulating.

Unlike group work, cooperative learning activities are highly structured, with defined roles, steps, and time limits. With transparent directions, every student knows exactly what to do. Another key difference is that these structures promote built-in accountability and equal participation, requiring everyone to contribute through regular interaction and processing.

When considering a teacher’s pedagogical content knowledge, eliciting student feedback is paramount. Responding to end of course surveys, student reflections highlight the importance of the affective domain through several emerging pivotal themes. Students emphasized the significance of engaging, inclusive, and supportive learning environments. This aligns with findings from the literature, which posits that allowing students to apply course content in innovative and engaging ways fosters a deeper understanding and a heightened sense of ownership in learning (Singha & Singha, 2024). Given these findings, creating a classroom community through collaboration increases motivation and encourages students to develop their critical thinking skills and analyze problems more effectively.

Some students conveyed appreciation for interactive, relational, and consistently structured learning experiences. One student communicated, “I felt heard, valued, and important in this class… everyone truly has the opportunity to openly share without judgment.” These emotional and interpersonal experiences support long-term learning and boost goal-directed behaviors.

Another key theme was the acknowledgment of active, collaborative, and applied learning activities. One student articulated the need for relatable, real-world, authentic tasks, stating, “My favorite activity was creating videos to connect with the assignments in class.” This reflection highlights the notion that students in higher education value opportunities that enable them to transition from passive consumers of information to active participants in the learning process (Ribeiro-Silva et al., 2022). Moreover, recent research supports the implementation of faculty professional development programs focused on active learning instruction and engaging students in college classrooms (Park & Xu, 2024).

Implications for Faculty

To design assignments that work and adopt a reflective framework in your own instruction, consider implementing the following practices:
1. Set Clear Learning Objectives: Ensure students understand what they are expected to accomplish during collaboration. Cooperative learning is most effective when aligned with specific outcomes, especially those that require higher-order thinking (analysis, synthesis, and application). Identify what students should know, do, or be able to explain by the end of the activity.
1. Design Purposeful and Flexible Groups: Keep it interesting and diversify student groups. Avoid simple tasks that can be done individually—design work that fosters interdependence and leverages diverse perspectives. Create tasks that are complex enough to require collaboration (e.g., solving a case study, evaluating evidence, or designing a project).
1. Structure Group Roles and Expectations: Assign roles based on instructional content to ensure equal participation and engagement among group members. Structured roles promote engagement and accountability. Rotate roles to ensure individual accountability and contribution.
1. Monitor and Support Groups in Real Time: Circulate during group work, check for understanding, and intervene as needed to guide discussion or clarify misconceptions. Guide communication, active listening, respectful disagreement, and group decision-making. Active monitoring shows students their work matters and allows faculty to model and scaffold skills often.
1. Encourage Metacognition: Provide opportunities for students to reflect on their group work experiences and the processes involved. Reflection reinforces academic content and collaboration skills.
By implementing collaborative techniques, faculty can motivate students to apply content knowledge that will prepare their future success in relatable, real-world contexts and encourage the development and maintenance of the interpersonal skills necessary for lifelong learning. Furthermore, by refining our pedagogical practices, we can foster a more robust society with confident, culturally competent, and self-aware leaders of tomorrow.

Final Reflections

Former U.S. Secretary of Education Miguel Cardona quoted the African proverb, “If you want to go fast, go alone. If you want to go far, go together.” University settings often overlook effective pedagogical approaches that incorporate collaboration, limiting opportunities for meaningful learning experiences that enhance understanding, sustain motivation, and spark intellectual curiosity. By designing, facilitating, monitoring, and evaluating inclusive learning spaces, we can contribute to a sense of belonging and bring joy back into the classroom.

Dr. Ana Figueroa is an assistant professor of education at the University of Tampa and the Lead Instructor of the Education Foundations and Human Exceptionalities courses in the undergraduate program. Her research interests include differentiated instruction, progress monitoring, teacher mindset, and highly effective instructional strategies. She champions equitable instruction for all learners.

References

Kagan, Spencer, and Miguel Kagan. Kagan Cooperative Learning. San Clemente, CA: Kagan Publishing, 2021.

Namahoe, K. (2022, April 15). Secretary Cardona’s 6 Pathways Forward in Education. ascd.org. December 29, 2024, https://ascd.org/blogs/secretary-cardonas-6-pathways-forward-in-education

Park, Elizabeth S., and Di Xu. “The Effect of Active Learning Professional Development Training on College Students’ Academic Outcomes.” Journal of Research on Educational Effectiveness 17, no. 1 (December 20, 2024): 43–64. https://doi.org/10.1080/19345747.2022.2151954.

Ribeiro-Silva, Elsa, Catarina Amorim, José Luis Aparicio-Herguedas, and Paula Batista. “Trends of Active Learning in Higher Education and Students’ Well-Being: A Literature Review.” Frontiers in Psychology 13 (April 18, 2022). https://doi.org/10.3389/fpsyg.2022.844236.

“Salesforce,” October 6, 2022. https://www.salesforce.com/news/stories/higher-education-statistics-trends-2022/.

Singha, Ranjit, and Surjit Singha. “Educational Innovation Transforming Higher Education for Workforce Readiness.” Advances in Higher Education and Professional Development, January 29, 2024, 37–55. https://doi.org/10.4018/979-8-3693-0517-1.ch003.
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July 16, 2025